Amazon SageMaker multi-model endpoints (MMEs) are a fully managed capability of SageMaker inference that allows you to deploy thousands of models on a single...
Manpower, budget, and time!!AI technology has been invaluable to businesses in all sectors. Over the past year, AI has become even more impactful.According to...
Today, we are excited to announce that Code Llama foundation models, developed by Meta, are available for customers through Amazon SageMaker JumpStart to deploy...
Introduction
Data Science is everywhere in the 21st century and has emerged as an innovative field. But what exactly is Data Science? And why should...
The Growing Demand for Machine Learning: Is it Surpassing Moore's Law?
Machine learning has become one of the most sought-after technologies in recent years, with...
Advancements in Machine Learning: Continuous Improvement and Progress
Machine learning, a subset of artificial intelligence, has witnessed remarkable advancements in recent years. From self-driving cars...
Introduction
Creating your own dataset is crucial in many data science and machine learning projects. While there are numerous publicly available datasets, building your own...
In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model...
Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker...
